Detection and Explanation of Anomalous Activities: Representing Activities as Bags of Event N-Grams
Abstract
We present a novel representation and method for detecting and explaining anomalous activities in a video stream. Drawing from natural language processing, we introduce a representation of activities as bags of event n-grams, where we analyze the global structural information of activities using their local event statistics. We demonstrate how maximal cliques in an undirected edge-weighted graph of activities, can be used in an unsupervised manner, to discover regular sub-classes of an activity class. Based on these discovered sub-classes, we formulate a definition of anomalous activities and present a way to detect them. Finally, we characterize each discovered sub-class in terms of its "most representative member" and present an information-theoretic method to explain the detected anomalies in a human-interpretable form.
Cite
Text
Hamid et al. "Detection and Explanation of Anomalous Activities: Representing Activities as Bags of Event N-Grams." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005. doi:10.1109/CVPR.2005.127Markdown
[Hamid et al. "Detection and Explanation of Anomalous Activities: Representing Activities as Bags of Event N-Grams." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005.](https://mlanthology.org/cvpr/2005/hamid2005cvpr-detection/) doi:10.1109/CVPR.2005.127BibTeX
@inproceedings{hamid2005cvpr-detection,
title = {{Detection and Explanation of Anomalous Activities: Representing Activities as Bags of Event N-Grams}},
author = {Hamid, Raffay and Johnson, Amos Y. and Batta, Samir and Bobick, Aaron F. and Jr., Charles L. Isbell and Coleman, Graham},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2005},
pages = {1031-1038},
doi = {10.1109/CVPR.2005.127},
url = {https://mlanthology.org/cvpr/2005/hamid2005cvpr-detection/}
}